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@INPROCEEDINGS{Aiello2000a,
  author = {William Aiello and Fan Chung and Linyuan Lu},
  title = {A random graph model for massive graphs},
  booktitle = {STOC '00: Proceedings of the thirty-second annual ACM symposium on
	Theory of computing},
  year = {2000},
  pages = {171--180},
  address = {New York, NY, USA},
  publisher = {ACM},
  note = {ACL Model},
  doi = {http://doi.acm.org/10.1145/335305.335326},
  file = {Aiello et al - A Random Graph Model for Massive Graphs (2000).pdf:artigos/Aiello
	et al - A Random Graph Model for Massive Graphs (2000).pdf:PDF},
  isbn = {1-58113-184-4},
  location = {Portland, Oregon, United States},
  owner = {rodrigo},
  timestamp = {2009.01.11}
}

@ARTICLE{Aiello2000b,
  author = {William Aiello and Fan Chung and Linyuan Lu},
  title = {A random graph model for power law graphs},
  journal = {Experimental Math},
  year = {2000},
  volume = {10},
  pages = {53--66},
  file = {Aiello et al - A Random Graph Model for Power Law Graphs (2000).pdf:artigos/Aiello
	et al - A Random Graph Model for Power Law Graphs (2000).pdf:PDF},
  owner = {rodrigo},
  timestamp = {2009.01.11}
}

@ARTICLE{Albert2000,
  author = {Reka Albert and Albert-Laszlo Barabasi},
  title = {Topology of evolving networks: local events and universality},
  journal = {Physical Review Letters},
  year = {2000},
  volume = {85},
  pages = {5234},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0005085}
}

@ARTICLE{Albert2002,
  author = {Reka Albert and Albert-László Barabási},
  title = {Statistical mechanics of complex networks},
  journal = {Reviews of Modern Physics},
  year = {2002},
  volume = {74},
  pages = {47},
  note = {Revisão de redes complexas. É como um Linked para o público acadêmico.},
  abstract = {Complex networks describe a wide range of systems in nature and society,
	much quoted examples including the cell, a network of chemicals linked
	by chemical reactions, or the Internet, a network of routers and
	computers connected by physical links. While traditionally these
	systems were modeled as random graphs, it is increasingly recognized
	that the topology and evolution of real networks is governed by robust
	organizing principles. Here we review the recent advances in the
	field of complex networks, focusing on the statistical mechanics
	of network topology and dynamics. After reviewing the empirical data
	that motivated the recent interest in networks, we discuss the main
	models and analytical tools, covering random graphs, small-world
	and scale-free networks, as well as the interplay between topology
	and the network's robustness against failures and attacks.},
  file = {Barabasi e Albert - Statistical mechanics of complex networks (2002).pdf:Para
	categorizar/Barabasi e Albert - Statistical mechanics of complex
	networks (2002).pdf:PDF},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0106096}
}

@ARTICLE{Amaral00,
  author = {Amaral, L. A. and Scala, A. and Barthelemy, M. and Stanley, H. E.},
  title = {Classes of small-world networks},
  journal = {Proceedings of the National Academy of Sciences},
  year = {2000},
  volume = {97},
  pages = {11149--11152},
  number = {21},
  abstract = {We study the statistical properties of a variety of diverse real-world
	networks. We present evidence of the occurrence of three classes
	of small-world networks: (a) scale-free networks, characterized by
	a vertex connectivity distribution that decays as a power law; (b)
	broad-scale networks, characterized by a connectivity distribution
	that has a power law regime followed by a sharp cutoff; and (c) single-scale
	networks, characterized by a connectivity distribution with a fast
	decaying tail. Moreover, we note for the classes of broad-scale and
	single-scale networks that there are constraints limiting the addition
	of new links. Our results suggest that the nature of such constraints
	may be the controlling factor for the emergence of different classes
	of networks.},
  citeulike-article-id = {3339778},
  keywords = {networks, small-world},
  posted-at = {2008-09-26 13:27:50},
  priority = {0},
  review = {= Citações =
	
	
	... both types of contraints [aging and cost constraints] lead to
	cut-offs on the power-law decay of the tail of connectivity distributions
	and that for strong enough constraints no power-law region is visible.}
}

@ARTICLE{Andrade2008,
  author = {Andrade, Roberto F. S. and Miranda, José G. V. and Pinho, Suani T.
	R. and Lobão, Thierry Petit},
  title = {Measuring distances between complex networks},
  journal = {Physics Letters A},
  year = {2008},
  volume = {372},
  pages = {5265--5269},
  number = {32},
  month = {August},
  abstract = {A previously introduced concept of higher order neighborhoods in complex
	networks, [R.F.S. Andrade, J.G.V. Miranda, T.P. Lobão, Phys. Rev.
	E 73 (2006) 046101] is used to define a distance between networks
	with the same number of nodes. With such measure, expressed in terms
	of the matrix elements of the neighborhood matrices of each network,
	it is possible to compare, in a quantitative way, how far apart in
	the space of neighborhood matrices two networks are. The distance
	between these matrices depends on both the network topologies and
	the adopted node numberings. While the numbering of one network is
	fixed, a Monte Carlo algorithm is used to find the best numbering
	of the other network, in the sense that it minimizes the distance
	between the matrices. The minimal value found for the distance reflects
	differences in the neighborhood structures of the two networks that
	arise only from distinct topologies. This procedure ends up by providing
	a projection of the first network on the pattern of the second one.
	Examples are worked out allowing for a quantitative comparison for
	distances among distinct networks, as well as among distinct realizations
	of random networks.},
  doi = {doi:10.1016/j.physleta.2008.06.044},
  file = {Garcia et al - Measuring distances between complex networks.pdf:novos/Garcia
	et al - Measuring distances between complex networks.pdf:PDF},
  keywords = {Complex network; Adjacency matrix; Neighborhood structure; Monte Carlo
	method},
  owner = {rodrigo},
  review = {= Resumo =
	
	
	O artigo propõe uma métrica de distância entre duas redes com o mesmo
	número de vértices. Essa métrica é usada no artigo para comparar
	diversos modelos generativos de redes.
	
	
	= Impressões =
	
	
	Artigo muito bem escrito!
	
	
	Essa idéia de distância pode ser usada para medir a distância de duas
	versões de um software. Consideramos na segunda versão apenas os
	vértices que já existiam na primeira.
	
	
	O artigo diz que a distância entre redes detecta diferenças locais
	que não são reveladas por comparação de propriedades geométricas
	globais (coeficiente de clustering, diâmetro, grau médio...)
	
	
	Essa distância entre redes é baseada na idéia de distância mínima
	entre vértices. E eu tenho a intuição que distância entre vértices
	não tem um significado bem definido no caso de redes de software.
	
	
	Dá pra pensar em distância entre redes baseada em outras métricas
	como coeficiente de clustering de um vértice (que é uma métrica sobre
	um vértice). Mas será que existe uma métrica que relacione dois vértices,
	sem ser distância, que é mais adequada para meu projeto? Talvez shared
	neighbors, ou ainda shared neighbors estendendo a noção de neighbors
	para vizinhos de alta ordem (vertices cuja distância é l, 1 <= l
	<= D, D é o diâmetro). Ou ainda a distância l para a qual os dois
	vértices possuem pelo menos m shared neighbors.},
  timestamp = {2009.02.11}
}

@ARTICLE{Barabasi2007,
  author = {Albert-Laszlo Barabasi},
  title = {The Architecture of Complexity: From Network Structure to Human Dynamics},
  journal = {Control Systems Magazine, IEEE},
  year = {2007},
  volume = {27},
  pages = {33--42},
  month = {August},
  doi = {10.1109/MCS.2007.384127},
  owner = {rodrigo},
  review = {The purpose of this article is to illustrate, through the example
	of human dynamics, that a thorough understanding of complex systems
	requires an understanding of network dynamics as well as network
	topology and architecture. After an overview of the topology of complex
	networks, such as the Internet and the WWW, data-driven models for
	human dynamics are given. These models motivate the study of network
	dynamics and suggest that complexity theory must incorporate the
	interactions between dynamics and structure. The article also advances
	the notion that an understanding of network dynamics is facilitated
	by the availability of large data sets and analysis tools gained
	from the study of network structure.},
  timestamp = {2009.04.15}
}

@ARTICLE{Barabasi1999,
  author = {Albert-Laszlo Barabasi and Reka Albert},
  title = {Emergence of scaling in random networks},
  journal = {Science},
  year = {1999},
  volume = {286},
  pages = {509},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/9910332}
}

@BOOK{Linked,
  title = {Linked: How Everything Is Connected to Everything Else and What It
	Means for Business, Science, and Everyday Life},
  publisher = {{Plume Books}},
  year = {2003},
  author = {Barabási, Albert-László},
  month = {April},
  abstract = {{A cocktail party. A terrorist cell. Ancient bacteria. An international
	conglomerate. <br><br> All are networks, and all are a part of a
	surprising scientific revolution. Albert-L\&aacuteszl\&oacute Barab\&aacutesi,
	the nation's foremost expert in the new science of networks, takes
	us on an intellectual adventure to prove that social networks, corporations,
	and living organisms are more similar than previously thought. Grasping
	a full understanding of network science will someday allow us to
	design blue-chip businesses, stop the outbreak of deadly diseases,
	and influence the exchange of ideas and information. Just as James
	Gleick brought the discovery of chaos theory to the general public,
	Linked tells the story of the true science of the future.}},
  citeulike-article-id = {105595},
  howpublished = {Paperback},
  isbn = {0452284392},
  keywords = {barabasi, book, linked, networks},
  posted-at = {2005-06-15 14:14:01},
  priority = {0},
  url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&amp;path=ASIN/0452284392}
}

@ARTICLE{Barabasi2003,
  author = {Albert-László Barabási and Eric Bonabeau},
  title = {Scale-Free Networks},
  journal = {Scientific American},
  year = {2003},
  pages = {50-59},
  month = {Mai},
  biburl = {http://www.bibsonomy.org/bibtex/2c05be45be07a46e34bf0674f3dda700e/sosbuch},
  entrytype = {article},
  file = {Barabasi e Bonabeau - Scale-Free Networks (Scientific American) (2003).pdf:artigos/Barabasi
	e Bonabeau - Scale-Free Networks (Scientific American) (2003).pdf:PDF;Deo
	et al - A Birth-death Dynamic Model of Scale-free Networks (2005).pdf:artigos/Deo
	et al - A Birth-death Dynamic Model of Scale-free Networks (2005).pdf:PDF;Valverde
	et al - Scale-free Networks from Optimal Design (2002).pdf:artigos/Valverde
	et al - Scale-free Networks from Optimal Design (2002).pdf:PDF;Barabasi
	et al - Determinist Scale-Free Networks.pdf:misc-artigos/Barabasi
	et al - Determinist Scale-Free Networks.pdf:PDF;Wu et al - Mining
	Scale-free Networks using Geodesic Clustering (2004).pdf:Para categorizar/Wu
	et al - Mining Scale-free Networks using Geodesic Clustering (2004).pdf:PDF},
  keywords = {imported }
}

@ARTICLE{Baxter2006,
  author = {Gareth Baxter and Marcus Frean and James Noble and Mark Rickerby
	and Hayden Smith and Matt Visser and Hayden Melton and Ewan Tempero},
  title = {Understanding the shape of Java software},
  journal = {SIGPLAN Not.},
  year = {2006},
  volume = {41},
  pages = {397--412},
  number = {10},
  abstract = {Large amounts of Java software have been written since the language's
	escape into unsuspecting software ecology more than ten years ago.
	Surprisingly little is known about the structure of Java programs
	in the wild: about the way methods are grouped into classes and then
	into packages, the way packages relate to each other, or the way
	inheritance and composition are used to put these programs together.
	We present the results of the first in-depth study of the structure
	of Java programs. We have collected a number of Java programs and
	measured their key structural attributes. We have found evidence
	that some relationships follow power-laws, while others do not. We
	have also observed variations that seem related to some characteristic
	of the application itself. This study provides important information
	for researchers who can investigate how and why the structural relationships
	we find may have originated, what they portend, and how they can
	be managed.},
  address = {New York, NY, USA},
  doi = {http://doi.acm.org/10.1145/1167515.1167507},
  file = {Baxter et al - Understanding the Shape of Java Software (2006).pdf:artigos/Baxter
	et al - Understanding the Shape of Java Software (2006).pdf:PDF},
  issn = {0362-1340},
  publisher = {ACM},
  review = {= Revisão =
	
	
	O estudo envolve 56 aplicações escritas em Java e se propõe a reproduzir
	de forma mais extensiva o trabalho de Wheeldon e Counsell.
	
	
	Os autores sugerem que métricas as quais os programadores possuem
	alguma noção possuem um cutoff na distribuição (veja citação)
	
	
	= Citações =
	
	
	This suggests that ‘C’ relationships are more likely than ‘P’ relationships
	to have ‘truncated’ curves. We can generalise this to hypothesise
	that any metric that measures something that the programmer is inherently
	aware of will tend to have a ‘truncated’ curve, that is, not be a
	power-law. (p. 11)
	
	(nota: relações C têm a ver com grau de saída e P, com grau de entrada.)
	
	
	There is quite noticeable variation on the degree of fit between different
	applications.
	
	
	= Crítica =
	
	
	Bom tratamento estatístico. Fornece equações para lognormal e stretched
	exponential.
	
	
	Leitura complicada...}
}

@INPROCEEDINGS{Bollobas2003,
  author = {Béla Bollobás and Christian Borgs and Jennifer Chayes and Oliver
	Riordan},
  title = {Directed scale-free graphs},
  booktitle = {SODA '03: Proceedings of the fourteenth annual ACM-SIAM symposium
	on Discrete algorithms},
  year = {2003},
  pages = {132--139},
  address = {Philadelphia, PA, USA},
  publisher = {Society for Industrial and Applied Mathematics},
  file = {Bollobas et al - Directed Scale-Free Graphs.pdf:artigos/Bollobas et
	al - Directed Scale-Free Graphs.pdf:PDF},
  isbn = {0-89871-538-5},
  location = {Baltimore, Maryland},
  owner = {rodrigo},
  review = {O artigo introduz um modelo para grafos orientados livres de escala
	que crescem com preferential attachment.
	
	A cada passo, o grafo pode ser modificado adicionando-lhe um vértice
	(que é ligado a um vértice existente) ou uma aresta entre dois vértices
	pré-existentes.
	
	Os autores fornecem demonstrações formais de que o modelo gera um
	grafo livre de escala.
	
	Por fim, são fornecidos parâmetros para o modelo que geram grafos
	semelhantes à estrutura da web.
	
	
	A meu ver, esse modelo possui duas vantagens em relação ao modelo
	de Barabasi-Albert:
	
	1) O grau de saída não é fixo.
	
	2) As novas arestas podem ligar vértices nos primeiros passos.
	
	
	No entanto, existem limitações:
	
	1) Não há remoção de arestas
	
	2) Não há remoção de vértices
	
	
	-- Rodrigo, 19/11/2008},
  timestamp = {2008.11.19}
}

@MISC{Challet2003,
  author = {Damien Challet and Andrea Lombardoni},
  title = {Bug propagation and debugging in asymmetric software structures},
  year = {2003},
  abstract = {Software dependence networks are shown to be scale-free and asymmetric.
	We then study how software components are affected by the failure
	of one of them, and the inverse problem of locating the faulty component.
	Software at all levels is fragile with respect to the failure of
	a random single component. Locating a faulty component is easy if
	the failures only affect their nearest neighbors, while it is hard
	if the failures propagate further.},
  file = {Challet et al - Bug propagation and debugging in asymmetric software
	structures (2003).pdf:artigos/Challet et al - Bug propagation and
	debugging in asymmetric software structures (2003).pdf:PDF},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0306509}
}

@INPROCEEDINGS{Chatzigeorgiou2006,
  author = {Alexander Chatzigeorgiou and Nikolaos Tsantalis and George Stephanides},
  title = {Application of graph theory to OO software engineering},
  booktitle = {WISER '06: Proceedings of the 2006 international workshop on Workshop
	on interdisciplinary software engineering research},
  year = {2006},
  pages = {29--36},
  address = {New York, NY, USA},
  publisher = {ACM},
  doi = {http://doi.acm.org/10.1145/1137661.1137669},
  file = {Chatzigeorgiou - Application of Graph Theory to OO Software Engineering
	(2006).pdf:artigos/Chatzigeorgiou - Application of Graph Theory to
	OO Software Engineering (2006).pdf:PDF},
  isbn = {1-59593-409-X},
  location = {Shanghai, China}
}

@ARTICLE{Chen2008,
  author = {Tao Chen and Qing Gu and Shusen Wang and Xiaoan Chen and Daoxu Chen},
  title = {Module-based large-scale software evolution based on complex networks},
  journal = {8th IEEE International Conference on Computer and Information Technology},
  year = {2008},
  pages = {798---803},
  abstract = {Large-scale software systems usually consist of a huge number of modules,
	and have a series of releases along with these modules. This can
	be seen as software evolution. In recent years, researchers have
	put forward several models of software evolution by employing the
	theory of complex networks. In this paper, we put forward a refined
	model of software evolution based on the BA model: module-based evolution.
	We theoretically prove that the power-law degree distribution can
	be held in our model. We also build a tool to construct and analyze
	the class diagrams of JDK (Java Development Kits) evolved from version
	1.2 to 1.6. The class diagrams can be seen as complex networks under
	evolution. We apply the module-based evolution model to these complex
	networks and simulate the evolution of key network features such
	as average clustering coefficient and average path length. Compared
	with real networks, our model can precisely describe the evolution
	of these features, and be used to help developers understand the
	characteristics of large-scale software evolution.},
  owner = {rodrigo},
  review = {= Resumo =
	
	
	O artigo propõe um modelo evolutivo de rede de software baseado no
	modelo de Barabási-Albert e considerado a organização da rede em
	módulos. Nesse modelo a ligação preferencial é expandida com um parâmetro,
	alfa, que determina até que ponto um vértice se liga preferencialmente
	a vértices do mesmo módulo. A probabilidade de se escolher um vértice
	é denotada por pi_a. O número de módulos é um parâmetro, M. A cada
	iteração do algoritmo é executado um dos seguintes eventos:
	
	
	Evento 1: Com probabilidade p1 adiciona um vértice com e1 arestas,
	que é atribuído a um módulo escolhido aleatoriamente. As extremidades
	livres das arestas são ligadas a vértices escolhidos de acordo com
	a pi_a.
	
	
	Evento 2: Com probabilidade p2 são adicionados e2 arestas. Para cada
	aresta, uma extremidade é selecionada aleatoriamente e a outra é
	selecionada de acordo com pi_a.
	
	
	Evento 3: Com probabilidade p3 são religadas e3 arestas. A cada vez
	é escolhido um vértice aleatoriamente e uma de suas arestas é religa
	para um vértice escolhido de acordo com pi_a.
	
	
	Evento 4: Com probabilidade p4 são removidos e4 arestas escolhidas
	aleatoriamente. 
	
	
	Quando se refere a grau, o modelo quer dizer grau de entrada.
	
	
	Os autores provam analiticamente que a distribuição dos graus de entrada
	é livre de escala e fornece uma equação para determinar o expoente
	da distribuição a partir dos parâmetros.
	
	
	Com a finalidade de avaliar empiricamente o modelo, os autores consideram
	5 versões do JDK e extraem os deltas entre as versões: número de
	novos vértices e número de novas arestas. Eles consideram p3 = p4
	= 0 e então determinam os parâmetros do modelo para cada evolução
	do JDK. Eles validam as redes sintéticas comparando com as redes
	reais algumas métricas: número de arestas, coeficiente de clustering
	médio, comprimento do caminho médio e coeficiente da distribuição
	de graus de entrada.
	
	
	= Crítica =
	
	
	A rede de ligações entre módulos é potencialmente um grafo completo.
	
	
	Os resultados do experimento com o JDK não são contundentes.},
  timestamp = {2009.05.18}
}

@ARTICLE{Clauset2008,
  author = {Clauset, Aaron and Moore, Cristopher and Newman, M. E. J. },
  title = {Hierarchical structure and the prediction of missing links in networks},
  journal = {Nature},
  year = {2008},
  volume = {453},
  pages = {98--101},
  number = {7191},
  citeulike-article-id = {2739852},
  doi = {http://dx.doi.org/10.1038/nature06830},
  file = {Clauset et al - Hierarchical structure and the prediction of missing
	links in networks (2008).pdf:artigos/Clauset et al - Hierarchical
	structure and the prediction of missing links in networks (2008).pdf:PDF},
  issn = {0028-0836},
  keywords = {graph, hierarchy},
  posted-at = {2008-05-04 00:44:20},
  priority = {5},
  publisher = {Nature Publishing Group},
  url = {http://dx.doi.org/10.1038/nature06830}
}

@MISC{Clauset2006,
  author = {Aaron Clauset and Cristopher Moore and M.~E.~J. Newman},
  title = {Structural Inference of Hierarchies in Networks},
  year = {2006},
  note = {Algoritmo de clustering não-determinístico, baseado em maximização
	do likelihood através de Markov Chain Monte Carlo sampling},
  abstract = {One property of networks that has received comparatively little attention
	is hierarchy, i.e., the property of having vertices that cluster
	together in groups, which then join to form groups of groups, and
	so forth, up through all levels of organization in the network. Here,
	we give a precise definition of hierarchical structure, give a generic
	model for generating arbitrary hierarchical structure in a random
	graph, and describe a statistically principled way to learn the set
	of hierarchical features that most plausibly explain a particular
	real-world network. By applying this approach to two example networks,
	we demonstrate its advantages for the interpretation of network data,
	the annotation of graphs with edge, vertex and community properties,
	and the generation of generic null models for further hypothesis
	testing.},
  file = {Clauset et al - Structural Inference of Hierarchies in Networks (2006).pdf:artigos/Clauset
	et al - Structural Inference of Hierarchies in Networks (2006).pdf:PDF},
  url = {doi:10.1007/978-3-540-73133-7_1}
}

@ARTICLE{Clauset2004,
  author = {Aaron Clauset and M.~E.~J. Newman and Cristopher Moore},
  title = {Finding community structure in very large networks},
  journal = {Physical Review E},
  year = {2004},
  volume = {70},
  pages = {066111},
  url = {doi:10.1103/PhysRevE.70.066111}
}

@MISC{Clauset2007,
  author = {Aaron Clauset and Cosma Rohilla Shalizi and M.~E.~J. Newman},
  title = {Power-law distributions in empirical data},
  year = {2007},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0706.1062}
}

@ARTICLE{Concas2007,
  author = {Concas, Giulio and Marchesi, Michele and Pinna, Sandro and Serra,
	Nicola},
  title = {Power-Laws in a Large Object-Oriented Software System},
  journal = IEEE_J_SE,
  year = {2007},
  volume = {33},
  pages = {687--708},
  number = {10},
  abstract = {We present a comprehensive study of an implementation of the Smalltalk
	object oriented system, one of the first and purest object-oriented
	programming environment, searching for scaling laws in its properties.
	We study ten system properties, including the distributions of variable
	and method names, inheritance hierarchies, class and method sizes,
	system architecture graph. We systematically found Pareto - or sometimes
	log-normal - distributions in these properties. This denotes that
	the programming activity, even when modeled from a statistical perspective,
	can in no way be simply modeled as a random addition of independent
	increments with finite variance, but exhibits strong organic dependencies
	on what has been already developed. We compare our results with similar
	ones obtained for large Java systems, reported in the literature
	or computed by ourselves for those properties never studied before,
	showing that the behavior found is similar in all studied object
	oriented systems. We show how the Yule process is able to stochastically
	model the generation of several of the power-laws found, identifying
	the process parameters and comparing theoretical and empirical tail
	indexes. Lastly, we discuss how the distributions found are related
	to existing object-oriented metrics, like Chidamber and Kemerer's,
	and how they could provide a starting point for measuring the quality
	of a whole system, versus that of single classes. In fact, the usual
	evaluation of systems based on mean and standard deviation of metrics
	can be misleading. It is more interesting to measure differences
	in the shape and coefficients of the data?s statistical distributions.},
  doi = {10.1109/TSE.2007.1019},
  file = {Concas et al - Power-Laws in a Large Object-Oriented Software System
	(2007).pdf:artigos/Concas et al - Power-Laws in a Large Object-Oriented
	Software System (2007).pdf:PDF},
  issn = {0098-5589},
  keywords = {D.2.3.a Object-oriented programming, D.2.4.h Statistical methods,
	D.2.8.a Complexity measures, D.2.8.d Product metrics, D.2.8.e Software
	science, D.3.2.p Object-oriented languages, G.3.p Stochastic processes},
  owner = {rodrigo},
  review = {= Revisão =
	
	
	O artigo computa diversas métricas a partir da análise dos sistemas
	Smalltalk, JDK e Eclipse. As distribuições estatísticas dessas métricas
	seguem consistententemente distribuições de Pareto ou log-normal.
	
	
	Os autores sugerem que não faz sentido calcular a média e o desvio-padrão
	de métricas de software, uma vez que muitas delas seguem distribuições
	de cauda gorda (fat tail). Eles propõem média e expoente da cauda.
	
	
	== Correlações entre métricas ==
	
	
	Sistema estudado: Smalltalk.
	
	
	LOC X out-degree: 0.97
	
	Number of methods X LOC: 0.83
	
	Number of methods X out-degree: 0.79
	
	Number of subclasses X in-degree: 0.80
	
	
	== Distribuições ==
	
	
	Sistemas estudados: Smalltalk e, às vezes, JDK e Eclipse.
	
	http://en.wikipedia.org/wiki/Pearson%27s_chi-square_test
	
	
	Número de métodos por classe: log-normal.
	
	Número total de atributos uma classe (atributos imediatos + atributos
	herdados): log-normal.
	
	Número de subclasses por classe: power law.
	
	Freqüência de instance variable and method names: power law.
	
	Número de chamadas a um método: power law.
	
	Linhas de código por classe: log-normal. (good fit)
	
	In-degree: power law.
	
	Out-degree: log-normal (obs.: out-degree tem correlação com LOC).
	
	
	= Citações =
	
	
	In fact, the usual evaluation of systems based on mean and standard
	deviation of metrics can be misleading. It is more interesting to
	measure differences in the shape and coefficients of the data’s statistical
	distributions. (abstract)
	
	
	(...) a full statistical model of software production is a task never
	accomplished before and is obviously beyond the scope of this paper.
	(p. 2)
	
	
	* Random and proportional. The entity to be changed is chosen randomly
	as before, but the size of the increment is proportional to its present
	value. This model leads to log-normal distributions of properties,
	which semms to be a much more frequent case in real software systems.
	(p. 6)
	
	
	Interestingly, if deletion is performed proportionally to the current
	value of the properties, exactly like addition, and if deletions
	are made at a lower rate than additions, GPA demonstrates that the
	overall process is still a Yule process.
	
	
	This means that the log-normal or power-law behavior found in the
	out-links distribution by other authors is an artifact. They, in
	fact, measured the log-normal (or power-law) distribution of class
	sizes. (p. 17)
	
	
	= Crítica =
	
	
	O artigo apresenta uma boa base estatística.},
  timestamp = {2008.11.15}
}

@INPROCEEDINGS{Deo2005,
  author = {Narsingh Deo and Aurel Cami},
  title = {A birth-death dynamic model of scale-free networks},
  booktitle = {ACM-SE 43: Proceedings of the 43rd annual Southeast regional conference},
  year = {2005},
  pages = {26--27},
  address = {New York, NY, USA},
  publisher = {ACM},
  doi = {http://doi.acm.org/10.1145/1167253.1167260},
  file = {Deo et al - A Birth-death Dynamic Model of Scale-free Networks (2005).pdf:artigos/Deo
	et al - A Birth-death Dynamic Model of Scale-free Networks (2005).pdf:PDF},
  isbn = {1-59593-059-0},
  location = {Kennesaw, Georgia}
}

@ARTICLE{Dorogovtsev2002,
  author = {S.~N. Dorogovtsev and A.~V. Goltsev and J.~F.~F. Mendes},
  title = {Pseudofractal Scale-free Web},
  journal = {Physical Review E},
  year = {2002},
  volume = {65},
  pages = {066122},
  note = {Citado por Myers, Barabási},
  abstract = {We find that scale-free random networks are excellently modeled by
	a deterministic graph. This graph has a discrete degree distribution
	(degree is the number of connections of a vertex) which is characterized
	by a power-law with exponent γ=1+łn3/łn2. Properties of this simple
	structure are surprisingly close to those of growing random scale-free
	networks with γ in the most interesting region, between 2 and 3.
	We succeed to find exactly and numerically with high precision all
	main characteristics of the graph. In particular, we obtain the exact
	shortest-path-length distribution. For the large network (łn N gg
	1) the distribution tends to a Gaussian of width ∼ √łn N centered
	at ell ∼ łn N. We show that the eigenvalue spectrum of the adjacency
	matrix of the graph has a power-law tail with exponent 2+γ.},
  comment = {5 pages, 3 figures},
  file = {Dorogovtsev, Goltsev, Mendes - Pseudofractal Scale-free Web (2001).pdf:artigos/Dorogovtsev,
	Goltsev, Mendes - Pseudofractal Scale-free Web (2001).pdf:PDF},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0112143}
}

@MISC{Drappa2000,
  author = {Drappa, A. and Ludewig, J.},
  title = {Simulation in software engineering training},
  year = {2000},
  doi = {10.1109/ICSE.2000.870411},
  file = {Drappa et al - Simulation in software engineering training (2000).pdf:artigos/Drappa
	et al - Simulation in software engineering training (2000).pdf:PDF},
  journal = {Software Engineering, 2000. Proceedings of the 2000 International
	Conference on},
  keywords = {computer based training, computer science education, digital simulation,
	project management, software development management, teachingQA model,
	SESAM project, application areas, future work, model behavior, quick-motion
	mode, simulated software project, simulation model, software development,
	software engineering education, software engineering training, software
	project manager, student},
  pages = {199-208}
}

@MISC{Eppstein2002,
  author = {David Eppstein and Joseph Wang},
  title = {A steady state model for graph power laws},
  year = {2002},
  note = {algoritmo implementado pela API JUNG},
  abstract = {Power law distribution seems to be an important characteristic of
	web graphs. Several existing web graph models generate power law
	graphs by adding new vertices and non-uniform edge connectivities
	to existing graphs. Researchers have conjectured that preferential
	connectivity and incremental growth are both required for the power
	law distribution. In this paper, we propose a different web graph
	model with power law distribution that does not require incremental
	growth. We also provide a comparison of our model with several others
	in their ability to predict web graph clustering behavior.},
  timestamp = {2009.02.18},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0204001}
}

@ARTICLE{Erdos1959,
  author = {Erd\"{o}s, P. and R\'{e}nyi, A. },
  title = {On random graphs, I},
  journal = {Publicationes Mathematicae (Debrecen)},
  year = {1959},
  volume = {6},
  pages = {290--297},
  citeulike-article-id = {4012374},
  keywords = {graphs, random},
  posted-at = {2009-02-28 00:47:14},
  priority = {2},
  url = {http://www.renyi.hu/\~{}p\_erdos/Erdos.html\#1959-11}
}

@ARTICLE{Fagiolo2006,
  author = {Giorgio Fagiolo},
  title = {Clustering in Complex Directed Networks},
  year = {2006},
  abstract = {Many empirical networks display an inherent tendency to cluster, i.e.
	to form circles of connected nodes. This feature is typically measured
	by the clustering coefficient (CC). The CC, originally introduced
	for binary, undirected graphs, has been recently generalized to weighted,
	undirected networks. Here we extend the CC to the case of (binary
	and weighted) directed networks and we compute its expected value
	for random graphs. We distinguish between CCs that count all directed
	triangles in the graph (independently of the direction of their edges)
	and CCs that only consider particular types of directed triangles
	(e.g., cycles). The main concepts are illustrated by employing empirical
	data on world-trade flows.},
  owner = {rodrigo},
  timestamp = {2009.02.12},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:physics/0612169}
}

@ARTICLE{Fu2006,
  author = {Peihua Fu and Kun Liao},
  title = {An Evolving Scale-free Network with Large Clustering Coefficient},
  journal = {Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International
	Conference on},
  year = {2006},
  pages = {1-4},
  month = {Dec.},
  note = {Relatively Preferential Attachment (RPA)},
  abstract = {Preferential attachment is generally regarded as the best mechanism
	to form scale-free networks. However, the simulated network has a
	much smaller clustering coefficient, while many networks in the real
	world, such as movie actors' collaboration and co-authorship networks,
	have a high clustering coefficient. So we develop the relatively
	preferential attachment (RPA) method which considers preferential
	attachment as well as the probability channel. RPA model can produce
	networks which not only keep the scale free property but also have
	high clustering coefficient close to those of real networks},
  doi = {10.1109/ICARCV.2006.345053},
  file = {Fu et al - An Evolving Scale-free Network with Large Clustering Coefficient
	(2006).pdf:artigos/Fu et al - An Evolving Scale-free Network with
	Large Clustering Coefficient (2006).pdf:PDF},
  keywords = {computer networks, probabilityclustering coefficient, evolving scale-free
	network, probability channel, relatively preferential attachment},
  owner = {rodrigo},
  timestamp = {2009.01.11}
}

@MISC{Gulbahce2008,
  author = {Natali Gulbahce and Sune Lehmann},
  title = {The art of community detection},
  year = {2008},
  abstract = {Networks in nature possess a remarkable amount of structure. Via a
	series of data-driven discoveries, the cutting edge of network science
	has recently progressed from positing that the random graphs of mathematical
	graph theory might accurately describe real networks to the current
	viewpoint that networks in nature are highly complex and structured
	entities. The identification of high order structures in networks
	unveils insights into their functional organization. Recently, Clauset,
	Moore, and Newman, introduced a new algorithm that identifies such
	heterogeneities in complex networks by utilizing the hierarchy that
	necessarily organizes the many levels of structure. Here, we anchor
	their algorithm in a general community detection framework and discuss
	the future of community detection.},
  owner = {rodrigo},
  review = {= Citações =
	
	
	Currently the state of the art is to design an artificial network
	with the structural properties that one wants to detect (e.g. group
	strucutre) and then show that the algorithm being tested is able
	to detect such structures.
	
	
	It is possible to artificially remove (or add) links from a real network
	and measure how well the algorithm under study is able to accurately
	determine robust community structure. (cita "Robustness of community
	structure in networks")},
  timestamp = {2009.03.02},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0807.1833}
}

@INCOLLECTION{Han2006,
  author = {Han, Jie and Yu, Yong and Lin, Chenxi and Han, Dingyi and Xue, Gui-Rong
	},
  title = {A Hierarchical Model of Web Graph},
  year = {2006},
  pages = {790--797},
  abstract = {The pages on the World Wide Web and their hyperlinks induce a huge
	directed graph – the Web Graph. Many models have been brought up
	to explain the static and dynamic properties of the graph. Most of
	them pay much attention to the pages without considering their essential
	relations. In fact, Web pages are well organized in Web sites as
	a tree hierarchy. In this paper, we propose a hierarchical model
	of Web graph which exploits both link structure and hierarchical
	relations of Web pages. The analysis of the model reveals many properties
	about the evolution of pages, sites and the relation among them.},
  citeulike-article-id = {2568611},
  doi = {http://dx.doi.org/10.1007/11811305\_86},
  file = {Han et al - A Hierarchical Model of Web Graph (2006).pdf:artigos/Han
	et al - A Hierarchical Model of Web Graph (2006).pdf:PDF},
  journal = {Advanced Data Mining and Applications},
  keywords = {web},
  posted-at = {2008-03-21 03:59:41},
  priority = {2},
  url = {http://dx.doi.org/10.1007/11811305\_86}
}

@MISC{Hazelhurst2003,
  author = {Scott Hazelhurst and Anton Bergheim},
  title = {Introduction ESTSim: A tool for creating benchmarks for EST clustering
	algorithms},
  year = {2003},
  owner = {rodrigo},
  review = {= Citações =
	
	
	In summary, the best benchmarks for testing new clustering algorithms
	are data sets described in (1) [expert comparison] above. Testing
	the quality of new algorithms by comparing them to existing algorithms
	is attractive, but flawed. Since benchmarks of the form described
	in (1) above are very rare, we need to look at complementary techniques.
	(p. 2)
	
	
	We propose that artificial but realistic data sets should be used
	as complementary benchmarks
	
	
	The objective of ESTsim is to produce large amounts of artificial
	– but realistic – test data for testing the effectiveness of different
	distance measures used in clustering DNA (or related sequences).
	
	
	The artificial creation of ESTs in this way will lead to the creation
	of an EST set whose exact final clustering is known. So, when testing
	a new algorithm or measure, we can compare the output of the new
	algorithm with the known right answer.
	
	
	In addition, the use of artificial test data enables us to produce
	data with a range of different error models. Thus, if some measures
	are better than other measures in different circumstances we will
	be able to provide some insight. This would be difficult to test
	with real data.
	
	
	In summary we propose that as well as testing on real data, testing
	on artificial data would be very useful.
	
	
	The methodology used was to try to understand the biological processes
	and with the aid of a biologist draw the types of error curves. We
	then found convenient mathematical functions that simulated these
	curves and that could be easily programmed. (p. 3)},
  timestamp = {2009.02.14}
}

@INPROCEEDINGS{Herraiz2007,
  author = {Herraiz,, Israel and Gonzalez-Barahona,, Jesus M. and Robles,, Gregorio},
  title = {Towards a Theoretical Model for Software Growth},
  booktitle = {MSR '07: Proceedings of the Fourth International Workshop on Mining
	Software Repositories},
  year = {2007},
  pages = {21},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  doi = {http://dx.doi.org/10.1109/MSR.2007.31},
  isbn = {0-7695-2950-X},
  review = {= Resumo =
	
	
	Esse artigo estuda cerca de 700 mil arquivos fonte na linguagem C
	tirados do FreeBSD Ports e calcula, para cada um deles, diversas
	métricas de tamanho -- SLOC, LOC, número de funções, úmero de linhas
	de comentário, número de comentários e número de linhas em branco
	-- e complexidade -- complexidade ciclomática, número de retornos
	de função e quatro métricas de Halstead.
	
	
	O estudo encontrou distribuições double Pareto para todas as métricas
	consideradas e uma alta correlação entre quaisquer duas delas.
	
	
	= Comentário =
	
	
	O tratamento estatístico do artigo usa a regra do três sigma para
	remover outliers e usa quantile-quantile (q-q) plots para comparar
	os dados com distribuições teóricas. Através de q-q plots os autores
	constataram que o logaritmo dos dados se aproxima da curva normal,
	exceto nos valores extremos, o que revela uma distribuição de double
	Pareto: as caudas inferiores e superiores são power laws e o corpo
	da distribuição é lognormal.
	
	
	= Crítica =
	
	
	Os autores poderiam usar maximum likelihood estimation para estimar
	os parâmetros das distribuições double Pareto (se é que existe um
	estimador para essa distribuição). Eles também poderiam ter usado
	o teste de Kolmogorov–Smirnov como um teste goodness of fit para
	poderem afirmar com mais força que os dados encontrados se encaixam
	na distribuição double Pareto.}
}

@INPROCEEDINGS{Hyland-Wood2006,
  author = {David Hyland-Wood and David Carrington and Simon Kaplan},
  title = {Scale-Free Nature of Java Software Package, Class and Method Collaboration
	Graphs},
  booktitle = {Proceedings of the 5th International Symposium on Empirical Software
	Engineering, Rio de Janeiro, Brasil},
  year = {2006},
  file = {Hyland-Wood et al - Scale-Free Nature of Java Software Package, Class
	and Method Collaboration Graphs (2006).pdf:artigos/Hyland-Wood et
	al - Scale-Free Nature of Java Software Package, Class and Method
	Collaboration Graphs (2006).pdf:PDF},
  organisation = {ACM}
}

@INPROCEEDINGS{Ichii2008,
  author = {Ichii, M. and Matsushita, M. and Inoue, K. },
  title = {An Exploration of Power-Law in Use-Relation of Java Software Systems},
  booktitle = {Proc. 19th Australian Conference on Software Engineering ASWEC 2008},
  year = {2008},
  pages = {422--431},
  abstract = {A software component graph, where a node represents a component and
	an edge represents a use-relation between components, is widely used
	for analysis methods of software engineering. It is said that a graph
	is characterized by its degree distribution. In this paper, we investigate
	software component graphs composed of Java classes, to seek whether
	the degree distribution follows so-called the power-law, which is
	a fundamental characteristic of various kinds of graphs in different
	fields. We found that the in-degree distribution follows the power-law
	and the out-degree distribution does not follow the power-law. In
	a software component graph with about 180 thousand components, just
	a few of the components have more than ten thousand in-degrees while
	most of the components have only one or zero in-degree.},
  doi = {10.1109/ASWEC.2008.4483231},
  file = {Ichii et al - An Exploration of Power-law in Use-relation of Java
	Software Systems.pdf:artigos/Ichii et al - An Exploration of Power-law
	in Use-relation of Java Software Systems.pdf:PDF},
  issn = {1530-0803},
  keywords = {Java, object-oriented programming, software reusability, Java class,
	Java software system, degree distribution, power-law, software component
	graph, software engineering, Degree Distribution, Power-law, Scale-free
	Network, Software Component Graph},
  owner = {rodrigo},
  review = {= Revisão =
	
	
	Analisa vários sistemas escritos em Java. Os vértices são classes
	ou interfaces e as arestas são relações extends, implements, declara
	variável/membro/parâmetro, acessa variável, chama método, instancia
	objeto.
	
	
	Calcula distribuição de graus para a) sistemas de software, b) conjuntos
	de sistemas de software que dependem entre si e c) subconjuntos de
	um sistema de software escolhidos i) aleatoriamente, ii) a partir
	de um elemento pivô ou iii) a partir de uma palavra chave.
	
	
	Em todos os casos percebe-se power law no in-degree mas não no out-degree.
	Ele sugere que o out-degree segue uma distribuição double pareto
	(a cauda é muito pesada para ser lognormal), mas afirma que é preciso
	investigar mais.
	
	
	Adicionalmente, há uma correlação entre out-degree e métricas de complexidade
	(WMC) e tamanho (LOC).
	
	
	Cita vários trabalhos que o precederam.
	
	
	Usa o SPARS-J para calcular métricas e relacionamentos entre classes/interfaces.
	
	
	= Citações =
	
	
	We believe that it is possible to detect the changes of the software
	architecture, or to measure its stability by watching the changes
	of the set of the components which have the large in-degree. (p.
	8)},
  timestamp = {2008.11.15}
}

@INPROCEEDINGS{Kumar2000,
  author = {R. Kumar and P. Raghavan and S. Rajagopalan and D. Sivakumar and
	A. Tomkins and E. Upfal},
  title = {Stochastic models for the Web graph},
  booktitle = {FOCS '00: Proceedings of the 41st Annual Symposium on Foundations
	of Computer Science},
  year = {2000},
  pages = {57},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  note = {Copying model},
  file = {Kumar et al - Stochastic models for the web graph (copying model)
	(2000).pdf:artigos/Kumar et al - Stochastic models for the web graph
	(copying model) (2000).pdf:PDF},
  isbn = {0-7695-0850-2}
}

@MISC{Labelle2004,
  author = {Nathan LaBelle and Eugene Wallingford},
  title = {Inter-Package Dependency Networks in Open-Source Software},
  year = {2004},
  file = {LaBelle et al - Inter-Package Dependency Networks in Open-Source Software
	(2004).pdf:artigos/LaBelle et al - Inter-Package Dependency Networks
	in Open-Source Software (2004).pdf:PDF},
  review = {= Revisão =
	
	
	Os autores estudam dependências entre pacotes em dois repositórios
	de software livre: Debian GNU/Linux software repository e FreeBSD
	Ports Collection. Eles concluem que as redes estudadas são de mundo
	pequeno e a distribuição de graus segue a power law (embora não perfeitamente).
	
	
	= Crítica =
	
	
	Artigo pequeno com tratamento estatístico simplista.},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0411096}
}

@ARTICLE{Lancichinetti2009,
  author = {Andrea Lancichinetti and Santo Fortunato},
  title = {Benchmarks for testing community detection algorithms on directed
	and weighted graphs with overlapping communities},
  year = {2009},
  abstract = {Many complex networks display a mesoscopic structure with groups of
	nodes sharing many links with the other nodes in their group and
	comparatively few with nodes of different groups. This feature is
	known as community structure and encodes precious information about
	the organization and the function of the nodes. Many algorithms have
	been proposed but it is not yet clear how they should be tested.
	Recently we have proposed a general class of undirected and unweighted
	benchmark graphs, with heterogenous distributions of node degree
	and community size. An increasing attention has been recently devoted
	to develop algorithms able to consider the direction and the weight
	of the links, which require suitable benchmark graphs for testing.
	In this paper we extend the basic ideas behind our previous benchmark
	to generate directed and weighted networks with built-in community
	structure. We also consider the possibility that nodes belong to
	more communities, a feature occurring in real systems, like, e. g.,
	social networks. As a practical application, we show how modularity
	optimization performs on our new benchmark.},
  owner = {rodrigo},
  timestamp = {2009.05.18},
  url = {http://arxiv.org/abs/0904.3940}
}

@ARTICLE{Lancichinetti2008,
  author = {Lancichinetti, Andrea and Fortunato, Santo and Radicchi, Filippo},
  title = {Benchmark graphs for testing community detection algorithms},
  journal = {Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)},
  year = {2008},
  volume = {78},
  number = {4},
  abstract = {Community structure is one of the most important features of real
	networks and reveals the internal organization of the nodes. Many
	algorithms have been proposed but the crucial issue of testing, i.e.
	the question of how good an algorithm is, with respect to others,
	is still open. Standard tests include the analysis of simple artificial
	graphs with a built-in community structure, that the algorithm has
	to recover. However, the special graphs adopted in actual tests have
	a structure that does not reflect the real properties of nodes and
	communities found in real networks. Here we introduce a new class
	of benchmark graphs, that account for the heterogeneity in the distributions
	of node degrees and of community sizes. We use this new benchmark
	to test two popular methods of community detection, modularity optimization
	and Potts model clustering. The results show that the new benchmark
	poses a much more severe test to algorithms than standard benchmarks,
	revealing limits that may not be apparent at a first analysis.},
  citeulike-article-id = {3454707},
  posted-at = {2008-10-28 03:40:43},
  priority = {2},
  publisher = {APS},
  review = {= Citações =
	
	
	This race towards the ideal method [of community detection] aims at
	two main goals, i.e., improving the ACCURACY in the determination
	of meaningful modules and reducing the COMPUTATIONAL COMPLEXITY of
	the algorithm. (p. 1)
	
	
	Testing an algorithm essentially means analyzing a network with a
	well-defined community structure and recovering its communities.
	Ideally, one would like to have MANY INSTANCES of real networks whose
	MODULES are precise KNOWN, but this is unfortunately not the case.
	Therefore, the most extensive tests are performed on COMPUTER GENERATED
	networks, with a built-in community structure.
	
	
	In this paper we propose a realistic benchmark for community detection,
	that accounts for the heterogeneity of both degree and community
	size. Detecting communities on this class of graphs is a CHALLENGING
	task.
	
	
	= Crítica =
	
	
	O modelo que gera as redes com uma estrutura modular bem definida
	realmente é bom? Como podemos provar que ele é bom? 
	
	
	Me parece que esse conceito de clustering bom é muito dependente de
	domínio.
	
	
	Suponhamos que estamos interessados no conceito de módulo baseado
	em uma função objetivo global que encoraja ligações entre vértices
	de um módulo e desencoraja ligações entre vértices de módulos distintos
	(a métrica Modularization Quality do algoritmo Bunch). Quando o modelo
	gera uma rede, a estrutura de comunidades usada como referência realmente
	é a estrutura ótima? Como provar?
	
	
	Em suma, talvez o modelo beneficie uma noção de modularidade em prejuízo
	de outras.},
  timestamp = {2009.02.12},
  url = {http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal\&id=PLEEE8000078000004046110000001\&idtype=cvips\&gifs=yes}
}

@ARTICLE{Li2008,
  author = {Huan Li and Beibei Huang and Jinhu Lu},
  title = {Dynamical evolution analysis of the object-oriented software systems},
  journal = {Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on
	Computational Intelligence). IEEE Congress on},
  year = {2008},
  pages = {3030-3035},
  month = {June},
  doi = {10.1109/CEC.2008.4631207},
  file = {Li et al - Dynamical Evolution Analysis of the Object-Oriented Software
	Systems (2008).pdf:artigos/Li et al - Dynamical Evolution Analysis
	of the Object-Oriented Software Systems (2008).pdf:PDF;Dynamical
	Evolution Analysis of the Object-Oriented Software Systems.pdf:misc-artigos/Dynamical
	Evolution Analysis of the Object-Oriented Software Systems.pdf:PDF},
  keywords = {evolutionary computation, object-oriented methods, software engineeringdynamical
	evolution analysis, maintenance cost reduction, object-oriented software
	systems, software engineering, software evolution, software systems
	development},
  review = {= Revisão =
	
	
	Analisa a evolução de algumas métricas ao longo de diferentes versões
	de diversos sistemas escritos em Java.
	
	
	= Crítica =
	
	
	Muito mal escrito!}
}

@MISC{Li2005,
  author = {Lun Li and David Alderson and Reiko Tanaka and John C. Doyle and
	Walter Willinger},
  title = {Towards a Theory of Scale-Free Graphs: Definition, Properties, and
	Implications (Extended Version)},
  year = {2005},
  abstract = {Although the ``scale-free'' literature is large and growing, it gives
	neither a precise definition of scale-free graphs nor rigorous proofs
	of many of their claimed properties. In fact, it is easily shown
	that the existing theory has many inherent contradictions and verifiably
	false claims. In this paper, we propose a new, mathematically precise,
	and structural definition of the extent to which a graph is scale-free,
	and prove a series of results that recover many of the claimed properties
	while suggesting the potential for a rich and interesting theory.
	With this definition, scale-free (or its opposite, scale-rich) is
	closely related to other structural graph properties such as various
	notions of self-similarity (or respectively, self-dissimilarity).
	Scale-free graphs are also shown to be the likely outcome of random
	construction processes, consistent with the heuristic definitions
	implicit in existing random graph approaches. Our approach clarifies
	much of the confusion surrounding the sensational qualitative claims
	in the scale-free literature, and offers rigorous and quantitative
	alternatives.},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0501169}
}

@INPROCEEDINGS{Lopez2004,
  author = {Luis Lopez-Fernandez and Gregorio Robles and Jesus M. Gonzalez-Barahona},
  title = {Applying Social Network Analysis to the Information in CVS Repositories},
  booktitle = {Proceedings 1st International Workshop on Mining Software Repositories},
  year = {2004},
  pages = {101--105},
  month = {May},
  abstract = {The huge quantities of data available in the CVS repositories of large,
	long-lived libre (free, open source) software projects, and the many
	interrelationships among those data offer opportunities for extracting
	large amounts of valuable information about their structure, evolution
	and internal processes. Unfortunately, the sheer volume of that information
	renders it almost unusable without applying methodologies which highlight
	the relevant information for a given aspect of the project. In this
	paper, we propose the use of a well known set of methodologies (social
	network analysis) for characterizing libre software projects, their
	evolution over time and their internal structure. In addition, we
	show how we have applied such methodologies to real cases, and extract
	some preliminary conclusions from that experience.},
  description = {analysing libre software projects with network analysis explains basic
	concepts of social network analysis},
  file = {Lopez et al - Applying Social Network Analysis to the Information
	in CVS Repositories (2004).pdf:artigos/Lopez et al - Applying Social
	Network Analysis to the Information in CVS Repositories (2004).pdf:PDF},
  keywords = {2004 analysis basic cvs msr networks }
}

@ARTICLE{Louridas2008,
  author = {Panagiotis Louridas and Diomidis Spinellis and Vasileios Vlachos},
  title = {Power laws in software},
  journal = {ACM Trans. Softw. Eng. Methodol.},
  year = {2008},
  volume = {18},
  pages = {1--26},
  number = {1},
  abstract = {A single statistical framework, comprising power law distributions
	and scale-free networks, seems to fit a wide variety of phenomena.
	There is evidence that power laws appear in software at the class
	and function level. We show that distributions with long, fat tails
	in software are much more pervasive than previously established,
	appearing at various levels of abstraction, in diverse systems and
	languages. The implications of this phenomenon cover various aspects
	of software engineering research and practice.},
  address = {New York, NY, USA},
  doi = {http://doi.acm.org/10.1145/1391984.1391986},
  file = {Louridas et al - Power Laws in Software (2008).pdf:artigos/Louridas
	et al - Power Laws in Software (2008).pdf:PDF},
  issn = {1049-331X},
  publisher = {ACM},
  review = {= Revisão =
	
	
	Os autores analisam redes de dependências entre bibliotecas dinâmicas,
	pacotes FreeBSD, pacotes CPAN e classes de sistemas OO e encontram
	power laws na distribuição de graus de todas essas redes. Algumas
	distribuições não são bem power laws -- os autores conjecturam que
	sejam lognormal ou stretched exponential.
	
	
	Os autores citam trabalhos anteriores (p. 12, sec. 3.8) e procuram
	implicações de suas descobertas em tópicos como reuso, qualidade
	e otimização.
	
	
	= Citações =
	
	
	To avoid bias due to outliers, we worked with the complementary cumulative
	definition (2), in which outliers are subsumed, and then converted
	the results of the fit to the initial distribution.}
}

@ARTICLE{Luqi1990,
  author = {Luqi},
  title = {A Graph Model for Software Evolution},
  journal = {IEEE Trans. Softw. Eng.},
  year = {1990},
  volume = {16},
  pages = {917--927},
  number = {8},
  address = {Piscataway, NJ, USA},
  doi = {http://dx.doi.org/10.1109/32.57627},
  file = {Luqi - A Graph Model for Software Evolution (1990).pdf:artigos/Luqi
	- A Graph Model for Software Evolution (1990).pdf:PDF},
  issn = {0098-5589},
  publisher = {IEEE Press}
}

@ARTICLE{Ma2008,
  author = {Yutao Ma and Keqing He and Jing Liu},
  title = {Network Motifs in Object-Oriented Software Systems},
  journal = {CoRR},
  year = {2008},
  volume = {abs/0808.3292},
  abstract = {Nowadays, software has become a complex piece of work that may be
	beyond our control. Understanding how software evolves over time
	plays an important role in controlling software development processes.
	Recently, a few researchers found the quantitative evidence of structural
	duplication in software systems or web applications, which is similar
	to the evolutionary trend found in biological systems. To investigate
	the principles or rules of software evolution, we introduce the relevant
	theories and methods of complex networks into structural evolution
	and change of software systems. According to the results of our experiment
	on network motifs, we find that the stability of a motif shows positive
	correlation with its abundance and a motif with high Z score tends
	to have stable structure. These findings imply that the evolution
	of software systems is based on functional cloning as well as structural
	duplication and tends to be structurally stable. So, the work presented
	in this paper will be useful for the analysis of structural changes
	of software systems in reverse engineering.},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://arxiv.org/abs/0808.3292},
  file = {Ma et al - Network Motifs in Object-Oriented Software Systems (2008).pdf:artigos/Ma
	et al - Network Motifs in Object-Oriented Software Systems (2008).pdf:PDF}
}

@CONFERENCE{Marchesi2004,
  author = {Michele Marchesi and Sandro Pinna and Nicola Serra and Stefano Tuveri},
  title = {Power Laws in Smalltalk},
  booktitle = {ESUG Conference},
  year = {2004},
  address = {Kothen, Germany},
  month = {September},
  file = {Power laws in Smalltalk (slides).pdf:artigos/Power laws in Smalltalk
	(slides).pdf:PDF},
  howpublished = {MARCHESI M., S. PINNA, N. SERRA, S. TUVERI. “Power Laws in Smalltalk”,
	ESUG CONFERENCE, 6-12 September 2004, Kothen, Germany.},
  owner = {rodrigo},
  timestamp = {2008.11.15}
}

@ARTICLE{Milo2002,
  author = {Milo, R. and Shen-Orr, S. and Itzkovitz, S. and Kashtan, N. and Chklovskii,
	D. and Alon, U. },
  title = {Network motifs: simple building blocks of complex networks.},
  journal = {Science},
  year = {2002},
  volume = {298},
  pages = {824--827},
  number = {5594},
  month = {October},
  abstract = {Complex networks are studied across many fields of science. To uncover
	their structural design principles, we defined "network motifs,"
	patterns of interconnections occurring in complex networks at numbers
	that are significantly higher than those in randomized networks.
	We found such motifs in networks from biochemistry, neurobiology,
	ecology, and engineering. The motifs shared by ecological food webs
	were distinct from the motifs shared by the genetic networks of Escherichia
	coli and Saccharomyces cerevisiae or from those found in the World
	Wide Web. Similar motifs were found in networks that perform information
	processing, even though they describe elements as different as biomolecules
	within a cell and synaptic connections between neurons in Caenorhabditis
	elegans. Motifs may thus define universal classes of networks. This
	approach may uncover the basic building blocks of most networks.},
  address = {Departments of Physics of Complex Systems and Molecular Cell Biology,
	Weizmann Institute of Science, Rehovot, Israel 76100.},
  citeulike-article-id = {101},
  doi = {10.1126/science.298.5594.824},
  issn = {1095-9203},
  keywords = {comp, comprehensive},
  posted-at = {2008-08-18 08:06:07},
  priority = {2},
  url = {http://dx.doi.org/10.1126/science.298.5594.824}
}

@ARTICLE{Moura2003,
  author = {Alessandro P.~S. de Moura and Ying-Cheng Lai and Adilson E. Motter},
  title = {Signatures of small-world and scale-free properties in large computer
	programs},
  journal = {Physical Review E},
  year = {2003},
  volume = {68},
  pages = {017102},
  file = {Moura et al - Signatures of small-world and scale-free properties
	in large computer programs (2003).pdf:artigos/Moura et al - Signatures
	of small-world and scale-free properties in large computer programs
	(2003).pdf:PDF},
  review = {= Revisão =
	
	
	Vértices: arquivos .h (cabeçalho de C e C++). Arestas: dois vértices
	estão conectados se eles são simultaneamente incluídos por um mesmo
	arquivo fonte (.c, .cpp, .cc...)
	
	
	Os autores analisam quatro sistemas: Linux kernel, XFree86, Mozilla
	e GIMP. Eles calculam distribuição de graus (não-orientado), caminho
	mínimo médio e coeficiente de clustering médio. Eles concluem que
	as redes são redes de mundo pequeno: alto clustering (>> Crand) e
	baixo diâmetro (~= Drand).
	
	
	Na verdade a rede seria bipartite entre arquivos fonte e arquivos
	de cabeçalho. O artigo estuda uma projeção dessa rede bipartite no
	conjunto de arquivos de cabeçalho. Um projeção no outro sentido fornece
	os mesmos resultados, segundo os autores.},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0306609}
}

@ARTICLE{Myers2003,
  author = {Christopher R Myers},
  title = {Software systems as complex networks: structure, function, and evolvability
	of software collaboration graphs.},
  journal = {Phys Rev E Stat Nonlin Soft Matter Phys},
  year = {2003},
  volume = {68},
  pages = {046116},
  number = {4 Pt 2},
  month = {Oct},
  abstract = {Software systems emerge from mere keystrokes to form intricate functional
	networks connecting many collaborating modules, objects, classes,
	methods, and subroutines. Building on recent advances in the study
	of complex networks, I have examined software collaboration graphs
	contained within several open-source software systems, and have found
	them to reveal scale-free, small-world networks similar to those
	identified in other technological, sociological, and biological systems.
	I present several measures of these network topologies, and discuss
	their relationship to software engineering practices. I also present
	a simple model of software system evolution based on refactoring
	processes which captures some of the salient features of the observed
	systems. Some implications of object-oriented design for questions
	about network robustness, evolvability, degeneracy, and organization
	are discussed in the wake of these findings.},
  institution = {Cornell Theory Center, Rhodes Hall, Cornell University, Ithaca, New
	York 14853, USA.},
  owner = {rodrigo},
  pmid = {14683011},
  review = {== Degree distributions ==
	
	
	Segue power law com expoentes gamma_in (para graus de entrada) e gamma_out
	(para graus de saída).
	
	
	Sistemas OO (arestas: colaboração entre classes = herança e agregação)
	
	gamma_out ~ 3
	
	gamma_in ~ 2
	
	
	Sistemas procedimentais (arestas: call graph)
	
	gamma_in = gamma_out = 2.5
	
	
	== Degree correlation ==
	
	
	Nós com alto grau de entrada têm baixo grau de saída e vice-versa.
	
	
	== Clustering coefficient ==
	
	
	C(k) ~ k^{-1} <-- indicador da natureza hierárquica do software
	
	
	C(k) é a média do coeficiente de clustering para os nós com grau k
	(considerando o grafo não orientado)
	
	
	== Misc ==
	
	
	O artigo introduz um modelo de evolução de software baseado em refactorings.},
  timestamp = {2008.11.15}
}

@MANUAL{Newman2005,
  title = {Power laws, Pareto distributions and Zipf's law},
  author = {M.~E.~J. Newman},
  year = {2005},
  journal = {Contemporary Physics},
  pages = {323},
  url = {doi:10.1080/00107510500052444},
  volume = {46}
}

@ARTICLE{Newman2004b,
  author = {M.~E.~J. Newman},
  title = {Fast algorithm for detecting community structure in networks},
  journal = {Physical Review E},
  year = {2004},
  volume = {69},
  pages = {066133},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0309508}
}

@ARTICLE{Newman2003,
  author = {M. E. J. Newman},
  title = {The structure and function of complex networks},
  journal = {SIAM Review},
  year = {2003},
  volume = {45},
  pages = {167--256}
}

@ARTICLE{Newman2004a,
  author = {M.~E.~J. Newman and M. Girvan},
  title = {Finding and evaluating community structure in networks},
  journal = {Physical Review E},
  year = {2004},
  volume = {69},
  pages = {026113},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0308217}
}

@ARTICLE{Park2007,
  author = {Park, Juyong and Barabasi, Albert-Laszlo},
  title = {Distribution of node characteristics in complex networks},
  journal = {Proceedings of the National Academy of Sciences},
  year = {2007},
  pages = {0705081104+},
  month = {November},
  note = {how the node properties correlate with the underlying network topology},
  abstract = {Our enhanced ability to map the structure of various complex networks
	is increasingly accompanied by the possibility of independently identifying
	the functional characteristics of each node. Although this led to
	the observation that nodes with similar characteristics have a tendency
	to link to each other, in general we lack the tools to quantify the
	interplay between node properties and the structure of the underlying
	network. Here we show that when nodes in a network belong to two
	distinct classes, two independent parameters are needed to capture
	the detailed interplay between the network structure and node properties.
	We find that the network structure significantly limits the values
	of these parameters, requiring a phase diagram to uniquely characterize
	the configurations available to the system. The phase diagram shows
	a remarkable independence from the network size, a finding that,
	together with a proposed heuristic algorithm, allows us to determine
	its shape even for large networks. To test the usefulness of the
	developed methods, we apply them to biological and socioeconomic
	systems, finding that protein functions and mobile phone usage occupy
	distinct regions of the phase diagram, indicating that the proposed
	parameters have a strong discriminating power. 10.1073/pnas.0705081104},
  citeulike-article-id = {1884115},
  doi = {http://dx.doi.org/10.1073/pnas.0705081104},
  file = {Park e Barabasi - Distribution of node characteristics in complex
	networks (2007).pdf:artigos/Park e Barabasi - Distribution of node
	characteristics in complex networks (2007).pdf:PDF},
  keywords = {network\_motifs, networks},
  posted-at = {2007-11-14 05:59:06},
  priority = {2},
  review = {Li superficialmente.
	
	
	O artigo apresenta uma análise sobre a relação entre propriedades
	de nós em uma rede e a topologia dessa rede. Ele introduz métricas
	como diadicidade (diadicity) e heterofilia.
	
	
	IDÉIA: estudar diadicidade e heterofilia de sistemas de software usando
	como propriedade o tipo de entidade: classe ou interface.},
  url = {http://dx.doi.org/10.1073/pnas.0705081104}
}

@INPROCEEDINGS{Pennock02,
  author = {David M. Pennock and Gary W. Flake and Steve Lawrence and Eric J.
	Glover and C. Lee Giles},
  title = {Winners don’t take all: Characterizing the competition for links
	on the web},
  booktitle = {Proceedings of the National Academy of Sciences},
  year = {2002},
  pages = {5207--5211},
  note = {generaliza modelo BA},
  abstract = {As a whole, the World Wide Web displays a striking "rich get richer"
	behavior, with a relatively small number of sites receiving a disproportionately
	large share of hyperlink references and traffic. However, hidden
	in this skewed global distribution, we discover a qualitatively different
	and considerably less biased link distribution among subcategories
	of pages---for example, among all university homepages or all newspaper
	homepages. While the connectivity distribution over the entire web
	is close to a pure power law, we find that the distribution within
	specific categories is typically unimodal on a log scale, with the
	location of the mode, and thus the extent of the "rich get richer"
	phenomenon, varying across different categories. Similar distributions
	occur in many other naturally-occurring networks, including research
	paper citations, movie actor collaborations, and US power grid connections.
	A simple generative model, incorporating a mixture of preferential
	and uniform attachment, quantifies the degree to which the rich nodes
	grow richer, and how new (and poorly-connected) nodes can compete.
	The model accurately accounts for the true connectivity distributions
	of category-specific web pages, the web as a whole, and other social
	networks.},
  review = {Site: http://modelingtheweb.com/
	
	
	= Resumo =
	
	
	NEC researchers discovered that the degree of "rich get richer" or
	"winners take all" behavior varies in different categories and may
	be significantly less than previously thought. A new model has been
	developed which can be used to predict and analyze competition and
	diversity in different communities on the web.
	
	
	O modelo mistura preferential attachment e uniform attachment. Modelo
	de grafo não-orientado, mas pode ser generalizado para grafo orientado.
	
	
	A distribuição de graus dentro de uma categoria tem um corpo log-normal
	e uma cauda lei de potência.
	
	
	Generalized BA model: every vertex has at least some baseline probability
	of gaining an edge.
	
	
	== Citações ==
	
	
	Generic and category-specific degree distributions.
	
	
	The probability that a randomly selected web page has k links is proportional
	to k^-gama for large k.
	
	
	A power law distribution has a heavy tail, which drops much more slowly
	than the tail of a Gaussian distribution.
	
	
	... a few pages have enormous number of links -- enough to skew the
	mean well above the median.
	
	
	It is an open question exactly how peaked distributions for subsets
	of the web like those in Figs. 1 and 2 sum together to produce nearly
	pure power law for the web as a whole. We conjecture that the vast
	majority of subsets (or subsets containing the vast majority of the
	pages) exhibit a nearly zero mode and dominate this sum, although
	more investigation is needed.
	
	
	Dorogovtsev et al and Levene et al independently propose similar generalizations
	of the BA model (the addition of a uniform component), motivating
	it in part as a natural way to parametrize the power-law exponent.
	
	
	Albert and Barabási (24) have proposed their own extension of their
	original model. Their augmented model involves a parametrized mixture
	of three processes: vertex additions, edge additions, and edge rewirings.
	
	
	== Crítica ==
	
	
	O autor escolheu os vértices a partir de listagens. Por exemplo, uma
	rede foi formada pelos sites de jornais americanos listados em www.usnewspaperlinks.com,
	que provavelmente não representa todos os sites de jornais americanos.
	Assim, parece haver uma tendência (bias).
	
	
	== TODO ==
	
	
	ver se a mesma distribuição é encontrada em módulos de programas,
	ou em pacotes de distribuições Linux de determinada seção (devel,
	graphics etc.)
	
	
	Reler e anotar seção Related Models.},
  timestamp = {2009.02.10},
  url = {http://modelingtheweb.com/pennock-pnas-2002-weblinks.pdf}
}

@ARTICLE{Potanin2005,
  author = {Alex Potanin and James Noble and Marcus Frean and Robert Biddle},
  title = {Scale-free geometry in OO programs},
  journal = {Commun. ACM},
  year = {2005},
  volume = {48},
  pages = {99--103},
  number = {5},
  abstract = {Though conventional OO design suggests programs should be built from
	many small objects, like Lego bricks, they are instead built from
	objects that are scale-free, like fractals, and unlike Lego bricks.},
  address = {New York, NY, USA},
  doi = {http://doi.acm.org/10.1145/1060710.1060716},
  file = {Potanin et al - Scale-Free Geometry in OO Programs (2005) [communications].pdf:artigos/Potanin
	et al - Scale-Free Geometry in OO Programs (2005) [communications].pdf:PDF},
  issn = {0001-0782},
  publisher = {ACM},
  review = {= Revisão =
	
	
	O estudo analisa 8 sistemas em Java, C++, Self e Smalltalk.
	
	
	O estudo analisa 60 execuções de 35 sistemas escritos em Java, e também
	sistemas em C++, Self e Smalltalk.
	
	
	Aqui os autores estudam referências a objetos, em tempo de execução
	de programas Java. Eles concluem que os grafos de objetos são livres
	de escala (tanto para in- quanto para out-degree). Além disso, eles
	percebem que objetos com alto grau de saída possuem baixo grau de
	entrada e vice-versa.
	
	
	Frase de impacto: Objetos não são peças de Lego, pois não tem uma
	medida característica para in- ou out-degree.
	
	
	= Citações =
	
	
	Notably, no objects have both high in-degree and high out-degree;
	on the contrary, the objects with many incoming references have few
	outgoing references, and vice versa. This effect may be a consequence
	of widely shared data structures with many outgoing references (such
	as arrays) having a proxy object that hides the actual reference
	to the array the other objects that use it.
	
	
	unlike Lego bricks, objects within large programs have no characteristic
	scale.
	
	
	= Crítica =
	
	
	Não há um tratamento estatístico rigoroso.}
}

@ARTICLE{Ravasz2003,
  author = {Erzsebet Ravasz and Albert-Laszlo Barabasi},
  title = {Hierarchical Organization in Complex Networks},
  journal = {Physical Review E},
  year = {2003},
  volume = {67},
  pages = {026112},
  file = {Ravasz e Barabasi - Hierarchical organization in complex networks
	(2003).pdf:artigos/Ravasz e Barabasi - Hierarchical organization
	in complex networks (2003).pdf:PDF;Ravasz, Barabasi - Hierarchical
	organization in complex networks.pdf:misc-artigos/Ravasz, Barabasi
	- Hierarchical organization in complex networks.pdf:PDF},
  review = {O artigo apresenta um modelo determinístico de redes livres de escala
	hierárquicas e a partir dele deriva um modelo estocástico. A organização
	hierárquica é caracterizada pela distribuição do coeficiente de clustering,
	C(k), que obedece à lei C(k) ~ 1/k. Experimentos mostram que diversas
	redes possuem organização hierárquica -- rede de atores, rede de
	sinônimos, rede www, internet no domínio AS -- mas algumas outras
	-- internet no nível de roteador e rede elétrica -- não. Os autores
	atribuem a falta de organização hierárquica a restrições geográficas.
	Os autores apontam outros modelos que provavelmente obedecem à lei
	de escala C(k) ~ 1/k e concluem que o papel dos hubs em redes hierárquicas
	é unir os vários módulos coesos.
	
	
	== Citações
	
	
	the clustering coefficient of real networks is to a high degree independent
	of the number of nodes in the network (p. 1)
	
	
	modular: one can easily identify groups of nodes that are highly interconnected
	with each other, but have only a few or no links to nodes outside
	of the grop to which they belong to (p. 1)
	
	
	We argue that his scaling law quantifies the coexistence of a hierarchy
	of nodes with different degrees of clustering (p. 3)
	
	
	hierarchy is absent in networks with strong geographical constraints,
	as the limitation on the link length strongly constraints the network
	topology (p. 4)
	
	
	the fitness model [39, 40] displays a C(k) that appears to scale with
	k. (p. 5)
	
	
	there is evidence that the model of active nodes [41] obeys the scaling
	law (p. 5)
	
	
	the scaling of C(k) depends on the parameter p, which governs the
	rate at which new nodes connect to the neighbors of selected nodes.
	(p. 6)
	
	
	most networks have a modular topology, quantified by the high clustering
	coefficient they display. (p. 6)
	
	
	we should not think of modularity as the coexistence of relatively
	independent groups of nodes. (p. 6)
	
	
	the hubs play the important role of bridging the many small communities
	of clusters into a single, integrated network. (p. 6)},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0206130}
}

@ARTICLE{Ravasz2002,
  author = {Ravasz, E. and Somera, A. L. and Mongru, D. A. and Oltvai, Z. N.
	and Barab\'{a}si, A. L.},
  title = {Hierarchical organization of modularity in metabolic networks.},
  journal = {Science},
  year = {2002},
  volume = {297},
  pages = {1551--5},
  number = {5586},
  abstract = {Spatially or chemically isolated functional modules composed of several
	cellular components and carrying discrete functions are considered
	fundamental building blocks of cellular organization, but their presence
	in highly integrated biochemical networks lacks quantitative support.
	Here, we show that the metabolic networks of 43 distinct organisms
	are organized into many small, highly connected topologic modules
	that combine in a hierarchical manner into larger, less cohesive
	units, with their number and degree of clustering following a power
	law. Within Escherichia coli, the uncovered hierarchical modularity
	closely overlaps with known metabolic functions. The identified network
	architecture may be generic to system-level cellular organization.},
  citeulike-article-id = {2846539},
  file = {Ravasz et al - Hierarchical Organization of Modularity in Metabolic
	Networks (2002).pdf:artigos/Ravasz et al - Hierarchical Organization
	of Modularity in Metabolic Networks (2002).pdf:PDF},
  keywords = {file-import-08-05-30},
  posted-at = {2008-05-30 05:53:26},
  priority = {2}
}

@PHDTHESIS{Serra2005,
  author = {Nicola Serra},
  title = {Power Laws in Object Oriented System Architectures},
  school = {Università di Cagliari, Italy},
  year = {2005},
  owner = {rodrigo},
  timestamp = {2009.03.04}
}

@MASTERSTHESIS{Stopford2005,
  author = {Benjamin Stopford},
  title = {An Experimental Simulation of the Evolution of Software},
  school = {Univ. of London},
  year = {2005},
  month = {September},
  file = {Stopford - [Master Thesis] An Experimental Simulation of the Evolution
	of Software (2005).pdf:artigos/Stopford - [Master Thesis] An Experimental
	Simulation of the Evolution of Software (2005).pdf:PDF},
  owner = {rodrigo},
  timestamp = {2009.01.11}
}

@ARTICLE{Stopford2008,
  author = {Benjamin Stopford and Steve Counsell},
  title = {A framework for the simulation of structural software evolution},
  journal = {ACM Trans. Model. Comput. Simul.},
  year = {2008},
  volume = {18},
  pages = {1--36},
  number = {4},
  address = {New York, NY, USA},
  doi = {http://doi.acm.org/10.1145/1391978.1391983},
  file = {Stopford and Counsell - A Framework for the Simulation of Structural
	Software Evolution (2008).pdf:artigos/Stopford and Counsell - A Framework
	for the Simulation of Structural Software Evolution (2008).pdf:PDF},
  issn = {1049-3301},
  publisher = {ACM}
}

@ARTICLE{Stopford2006,
  author = {Benjamin Stopford and Steve Counsell},
  title = {Simulating the Structural Evolution of Software},
  year = {2006},
  volume = {3966/2006},
  pages = {294--301},
  book = {Software Process Change},
  doi = {10.1007/11754305_32},
  file = {Stopford and Counsell - Simulating the Structural Evolution of Software
	(2006).pdf:artigos/Stopford and Counsell - Simulating the Structural
	Evolution of Software (2006).pdf:PDF},
  issn = {1611-3349},
  owner = {rodrigo},
  publisher = {Springer Berlin / Heidelberg},
  timestamp = {2009.01.11}
}

@MISC{Tamai2002,
  author = {Tetsuo Tamai and Takako Nakatani},
  title = {Analysis of software evolution processes using statistical distribution
	Models},
  year = {2002},
  file = {Tamai et al - Analysis of software evolution processes using statistical
	distribution models (2002).pdf:artigos/Tamai et al - Analysis of
	software evolution processes using statistical distribution models
	(2002).pdf:PDF},
  owner = {rodrigo},
  timestamp = {2008.10.31}
}

@BOOK{Tan2005,
  title = {Introduction to Data Mining},
  publisher = {Addison Wesley},
  year = {2005},
  author = {Tan, Pang-Ning and Steinbach, Michael and Kumar, Vipin },
  edition = {1},
  month = {May},
  abstract = {Introduction to Data Mining presents fundamental concepts and algorithms
	for those learning data mining for the first time. Each major topic
	is organized into two chapters, beginning with basic concepts that
	provide necessary background for understanding each data mining technique,
	followed by more advanced concepts and algorithms.},
  citeulike-article-id = {594811},
  howpublished = {Hardcover},
  isbn = {0321321367},
  keywords = {kdd},
  posted-at = {2007-09-05 16:37:59},
  priority = {2},
  url = {http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20\&amp;path=ASIN/0321321367}
}

@ARTICLE{Valverde2002,
  author = {Valverde, S. and Ferrer Cancho, R. and Sol{\'e}, R.~V.},
  title = {Scale-free networks from optimal design},
  journal = {Europhysics Letters},
  year = {2002},
  volume = {60},
  pages = {512-517},
  month = nov,
  adsnote = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl = {http://adsabs.harvard.edu/abs/2002EL.....60..512V},
  doi = {10.1209/epl/i2002-00248-2},
  eprint = {arXiv:cond-mat/0204344},
  file = {Valverde et al - Scale-free Networks from Optimal Design (2002).pdf:artigos/Valverde
	et al - Scale-free Networks from Optimal Design (2002).pdf:PDF},
  review = {= Revisão =
	
	
	Os autores argumentam que as características emergentes de grafos
	de software (livres de escala e mundo pequeno) são o resultado de
	um processo de otimização local, e não de preferential attachment.
	Esse processo de otimização não é bem explicado.
	
	
	== Citações ==
	
	
	Although the rules that define the strategies involved in software
	engineering should lead to a tree-like structure, the final net is
	scale-free, perhaps reflecting the presence of conflicting constraints
	unavoidable in a multidimensional optimization process. (p.1, abstract)
	
	
	cost minimization together with optimal communication among units
	
	
	Essentially, they deal with optimal communication among modules and
	low cost (in terms of wiring) together with the
	
	rule of avoiding hubs (classes with large number of dependencies,
	that is, large degree).
	
	
	Intuitively, a trade-off between the number of nodes and the number
	of links must be chosen.
	
	
	plot: l log(<k>) vs. N, onde l é a média dos caminhos mais curtos.}
}

@ARTICLE{Valverde2005,
  author = {Valverde, Sergi and Solé, Ricard V.},
  title = {Network motifs in computational graphs: A case study in software
	architecture},
  journal = {Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)},
  year = {2005},
  volume = {72},
  number = {2},
  abstract = {Complex networks in both nature and technology have been shown to
	display characteristic, small subgraphs (so-called motifs) which
	appear to be related to their underlying functionality. All these
	networks share a common trait: they manipulate information at different
	scales in order to perform some kind of computation. Here we analyze
	a large set of software class diagrams and show that several highly
	frequent network motifs appear to be a consequence of network heterogeneity
	and size, thus suggesting a somewhat less relevant role of functionality.
	However, by using a simple model of network growth by duplication
	and rewiring, it is shown the rules of graph evolution seem to be
	largely responsible for the observed motif distribution.},
  citeulike-article-id = {2307657},
  comment = {8 pages},
  doi = {http://dx.doi.org/10.1103/PhysRevE.72.026107},
  keywords = {motif},
  posted-at = {2008-01-30 15:28:11},
  priority = {2},
  publisher = {APS},
  review = {Motifs (motivos) são subgrafos pequenos e característicos de um grafo.
	Este trabalho analisou 83 sistemas de software e encontrou diversos
	motivos frequentes comuns aos sistemas analisados e a redes de neurônios,
	circuitos eletrônicos e transcrição de genes, dentre os quais bi-fan,
	bi-parallel e feed forward loop. Ele também propôs um modelo de rede
	baseado em duplicação.
	
	
	Motivos podem estar associados a funcionalidades específicas, mas
	no caso de software, a formação dos motivos se deve a regras de evolução
	da rede. 
	
	
	Algumas conclusões do estudo:
	
	
	* A frequência de um motivo é proporcional ao tamanho da rede. (Fig.4,
	p. 5)
	
	* most common subgraphs are sparser than less common ones, which are
	more dense. 
	
	* Such a common point might be easily interpreted in functional terms:
	similar subgraphs are abundant because they are selected or chosen
	to perform a given function or task. As shown below, no evidence
	from statistical patterns supports such view.
	
	
	O artigo apresenta muita matemática (estatística) sobre motifs.
	
	
	Os autores propõem um modelo de rede baseado em duplicação:
	
	
	Primeiro é criado um grafo pequeno da seguinte forma: a cada passo
	é adicionado um nó com grau k_0 = 2, que se liga a vértices aleatoriamente.
	A seguir são aplicadas as seguintes regras:
	
	1) Duplicação: um vértice v, escolhido aleatoriamente, é clonado,
	e novo vértice w é ligado a todos os vértices aos quais v está ligado.
	
	2) Divergência: para cada par de arestas originais e redundantes,
	remova um deles com probabilidade delta.
	
	3) Cross linking: crie a ligação w -> v com probabilidade beta. Essa
	etapa é importante para a formação de 3-subgrafos.
	
	
	Experimentos reveleram que esse modelo é capaz de produzir os motivos
	frequentes.
	
	Esse modelo foi introduzido na referência [6].
	
	
	
	[6] R. V. Solé, R. Pastor-Satorras, E. D. Smith, and T. Kepler,
	
	Adv. Complex Syst. 5, 43 2002; A. Vazquez, A. Flammini,
	
	A. Maritan, and A. Vespignani, Complexus 1, 38 2003; R.
	
	Pastor-Satorras, E. D. Smith, and R. V. Solé, J. Theor. Biol.
	
	222, 199 2003; J. Kim, P. L. Krapivsky, B. Kahng, and S.
	
	Redner, Phys. Rev. E 66, 055101 2002; K.-I. Goh, B. Kahng,
	
	and D. Kim, e-print q-bio.MN/0312009, v2; W. Banzhaf and P.
	
	Dwigth Kuo, J. Biol. Phys. Chem. 4, 85 2004.},
  url = {http://dx.doi.org/10.1103/PhysRevE.72.026107}
}

@ARTICLE{Valverde2003,
  author = {Sergi Valverde and Ricard V. Solé},
  title = {Hierarchical Small Worlds in Software Architecture},
  year = {2003},
  number = {Directed Scale-Free Graphs},
  file = {Valverde et al - Hierarchical Small Worlds in Software Architecture
	(2003).pdf:artigos/Valverde et al - Hierarchical Small Worlds in
	Software Architecture (2003).pdf:PDF},
  review = {Alerta: este artigo tem muito blá-blá-blá. Pulando essa parte, a análise
	experimental é boa.
	
	
	Os autores analisam cerca de 30 sistemas escritos em C++. As arestas
	do grafo englobam agregação, herança e tipos de retorno. Eis os resultados
	experimentais:
	
	1) O número de arestas (L) cresce linearmente com o tamanho (número
	de vértices) do sistema (N): L ~ N^1.17
	
	2) O clustering coefficient está entre 0,084 e 0,413
	
	3) O diâmetro médio está entre 2,66 e 6,54
	
	4) Um valor típico de k médio (grau de entrada + grau de saída) é
	5 (tirado do jogo ProRally 2002)
	
	5) Considere a distribuição cumulativa dos graus como uma power-law
	com expoente a.
	
	 - Valores típicos de a para grau de entrada estão entre 0.94 e 1.55.
	
	 - Valores típicos de a para grau de saída estão entre 1.41 e 2.39.
	
	 - Valores típicos de a para entrada/saída estão entre 1.37 e 1.74.
	
	6) Seja T a quantidade de arestas no fecho transitivo do grafo. T
	~ N^1.46 (p. 8)
	
	
	"O modelo de Barabási-Albert não reproduz o alto clustering presente
	em muitos sistemas." (interessante, meus experimentos mostraram padrões
	razoáveis de clustering coefficient).
	
	
	Os autores analisam algumas métricas de evolução, mas não chegam a
	resultados conclusivos.
	
	
	"It has been noticed several times that in order to advance the current
	state of software engineering a scientific theory is required." --
	Lehman e Ramil: "An Approach to a Theory of Software Evolution" (2001)
	
	
	-- Rodrigo, 19/11/2008},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0307278}
}

@ARTICLE{Vazquez2003,
  author = {Vázquez, Alexei},
  title = {Growing network with local rules: Preferential attachment, clustering
	hierarchy, and degree correlations},
  journal = {Phys. Rev. E},
  year = {2003},
  volume = {67},
  pages = {056104},
  number = {5},
  month = {May},
  note = {duplication-divergence, connecting neighbors, random walk},
  doi = {10.1103/PhysRevE.67.056104},
  numpages = {15},
  owner = {rodrigo},
  publisher = {American Physical Society},
  timestamp = {2009.01.19}
}

@ARTICLE{Vazquez2003a,
  author = {A. Vázquez and A. Flammini and A. Maritan and A. Vespignani},
  title = {Modeling of protein interaction networks},
  journal = {Complexus},
  year = {2003},
  volume = {1},
  pages = {38},
  note = {duplication-divergence},
  file = {Vazquez et al - Modeling of protein interaction networks (2003).pdf:Vazquez
	et al - Modeling of protein interaction networks (2003).pdf:PDF},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cond-mat/0108043}
}

@ARTICLE{Watts1998,
  author = {Watts, D. J. and Strogatz, S. H. },
  title = {Collective dynamics of 'small-world' networks.},
  journal = {Nature},
  year = {1998},
  volume = {393},
  pages = {440--442},
  number = {6684},
  month = {June},
  abstract = {Networks of coupled dynamical systems have been used to model biological
	oscillators, Josephson junction arrays, excitable media, neural networks,
	spatial games, genetic control networks and many other self-organizing
	systems. Ordinarily, the connection topology is assumed to be either
	completely regular or completely random. But many biological, technological
	and social networks lie somewhere between these two extremes. Here
	we explore simple models of networks that can be tuned through this
	middle ground: regular networks 'rewired' to introduce increasing
	amounts of disorder. We find that these systems can be highly clustered,
	like regular lattices, yet have small characteristic path lengths,
	like random graphs. We call them 'small-world' networks, by analogy
	with the small-world phenomenon (popularly known as six degrees of
	separation. The neural network of the worm Caenorhabditis elegans,
	the power grid of the western United States, and the collaboration
	graph of film actors are shown to be small-world networks. Models
	of dynamical systems with small-world coupling display enhanced signal-propagation
	speed, computational power, and synchronizability. In particular,
	infectious diseases spread more easily in small-world networks than
	in regular lattices.},
  address = {Department of Theoretical and Applied Mechanics, Cornell University,
	Ithaca, New York 14853, USA. djw24@columbia.edu},
  citeulike-article-id = {99},
  doi = {10.1038/30918},
  issn = {0028-0836},
  keywords = {complex\_networks, complex\_systems, network, network\_generation\_model,
	small\_world},
  posted-at = {2007-10-10 03:41:03},
  priority = {5},
  url = {http://dx.doi.org/10.1038/30918}
}

@ARTICLE{Wen2007,
  author = {Lian Wen and Kirk, D. and Dromey, R.G.},
  title = {Software Systems as Complex Networks},
  journal = {Cognitive Informatics, 6th IEEE International Conference on},
  year = {2007},
  pages = {106-115},
  month = {Aug.},
  doi = {10.1109/COGINF.2007.4341879},
  file = {Wen et al - Software Systems as Complex Networks (2007).pdf:artigos/Wen
	et al - Software Systems as Complex Networks (2007).pdf:PDF;Myers
	- Software systems as complex networks, structure, function, and
	evolvability of software collaboration graphs (2003).pdf:artigos/Myers
	- Software systems as complex networks, structure, function, and
	evolvability of software collaboration graphs (2003).pdf:PDF},
  keywords = {Java, large-scale systems, software libraries, software maintenance,
	systems re-engineeringJava libraries, complex networks, component
	dependency network, scale-free network model, software maintenance,
	software reengineering, software systems}
}

@ARTICLE{Wheeldon2003,
  author = {Wheeldon, R. and Counsell, S.},
  title = {Power law distributions in class relationships},
  journal = {Source Code Analysis and Manipulation, 2003. Proceedings. Third IEEE
	International Workshop on},
  year = {2003},
  pages = { 45-54},
  month = {Sept.},
  abstract = {Power law distributions have been found in many natural and social
	phenomena, and more recently in the source code and run-time characteristics
	of Object-Oriented (OO) systems. A power law implies that small values
	are extremely common, whereas large values are extremely rare. We
	identify twelve new power laws relating to the static graph structures
	of Java programs. The graph structures analyzed represented different
	forms of OO coupling, namely, inheritance, aggregation, interface,
	parameter type and return type. Identification of these new laws
	provides the basis for predicting likely features of classes in future
	developments. The research ties together work in object-based coupling
	and World Wide Web structures.},
  issn = { },
  keywords = { Java, data flow graphs, inheritance, object-oriented programming,
	source coding Java program, World Wide Web structure, aggregation,
	inheritance, object-based coupling, object-oriented system, power
	law distribution, run-time characteristics, static graph structure}
}

@ARTICLE{Xu2006,
  author = {RenZuo Xu and XiaoDong Zhu and DaPeng Qi and Wei Huang and Wen Liu
	and Shuang Ming and AnCe Huang},
  title = {Investigation on Complex Networks in Software Engineering},
  journal = {Management of Innovation and Technology, 2006 IEEE International
	Conference on},
  year = {2006},
  volume = {1},
  pages = {532-534},
  month = {June },
  doi = {10.1109/ICMIT.2006.262238},
  file = {Xu et al - Investigation on Complex Networks in Software Engineering
	(2006).pdf:artigos/Xu et al - Investigation on Complex Networks in
	Software Engineering (2006).pdf:PDF;Investigation on Complex Networks
	in Software Engineering.txt:Para categorizar/Investigation on Complex
	Networks in Software Engineering.txt:Text},
  keywords = {configuration management, project management, software development
	managementcomplex network theory, finance management, manpower management,
	small world phenomenon, software development schedule management,
	software engineering, software project success rate, software version
	management}
}

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